A novel NOx prediction model using the parallel structure and convolutional neural networks for a coal-fired boiler

In this paper, a novel model with a parallel structure is proposed to predict NOx emissions from coal-fired boilers by using historical operational data, coal properties, and convolutional neural networks. The model inputs are processed and passed into three parallel subnetworks with well-designed building blocks. The features learned by the three subnetworks are fused and used to predict NOx emissions from a 330-MW pulverized coal-fired utility boiler. A comprehensive comparison of different prediction models based on deep learning algorithms shows that the prediction model proposed in this paper outperforms other prediction models in terms of root mean square error criteria. The results show that the parallel structure is key to obtaining accurate predictions while reducing model complexity. This suggests that the model's performance can be improved by designing the model architecture.